Alternatives to Julia logo

Alternatives to Julia

Python, R Language, MATLAB, Rust, and Golang are the most popular alternatives and competitors to Julia.
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What is Julia and what are its top alternatives?

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
Julia is a tool in the Languages category of a tech stack.
Julia is an open source tool with 39.1K GitHub stars and 4.9K GitHub forks. Here’s a link to Julia's open source repository on GitHub

Top Alternatives to Julia

  • Python
    Python

    Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best. ...

  • R Language
    R Language

    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. ...

  • MATLAB
    MATLAB

    Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. ...

  • Rust
    Rust

    Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory. ...

  • Golang
    Golang

    Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language. ...

  • NumPy
    NumPy

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

  • PHP
    PHP

    Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world. ...

Julia alternatives & related posts

Python logo

Python

172.6K
143.9K
6.6K
A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.
172.6K
143.9K
+ 1
6.6K
PROS OF PYTHON
  • 1.1K
    Great libraries
  • 937
    Readable code
  • 830
    Beautiful code
  • 774
    Rapid development
  • 677
    Large community
  • 422
    Open source
  • 381
    Elegant
  • 273
    Great community
  • 266
    Object oriented
  • 211
    Dynamic typing
  • 73
    Great standard library
  • 54
    Very fast
  • 51
    Functional programming
  • 39
    Easy to learn
  • 39
    Scientific computing
  • 32
    Great documentation
  • 25
    Productivity
  • 25
    Matlab alternative
  • 24
    Easy to read
  • 20
    Simple is better than complex
  • 18
    It's the way I think
  • 17
    Imperative
  • 15
    Free
  • 15
    Very programmer and non-programmer friendly
  • 14
    Powerfull language
  • 14
    Powerful
  • 13
    Fast and simple
  • 12
    Scripting
  • 12
    Machine learning support
  • 9
    Explicit is better than implicit
  • 8
    Ease of development
  • 8
    Unlimited power
  • 8
    Clear and easy and powerfull
  • 7
    Import antigravity
  • 6
    It's lean and fun to code
  • 6
    Print "life is short, use python"
  • 5
    Great for tooling
  • 5
    There should be one-- and preferably only one --obvious
  • 5
    Python has great libraries for data processing
  • 5
    High Documented language
  • 5
    I love snakes
  • 5
    Although practicality beats purity
  • 5
    Flat is better than nested
  • 5
    Fast coding and good for competitions
  • 4
    Readability counts
  • 3
    Lists, tuples, dictionaries
  • 3
    CG industry needs
  • 3
    Now is better than never
  • 3
    Multiple Inheritence
  • 3
    Great for analytics
  • 3
    Complex is better than complicated
  • 3
    Plotting
  • 3
    Beautiful is better than ugly
  • 3
    Rapid Prototyping
  • 3
    Socially engaged community
  • 2
    List comprehensions
  • 2
    Web scraping
  • 2
    Many types of collections
  • 2
    Ys
  • 2
    Easy to setup and run smooth
  • 2
    Generators
  • 2
    Special cases aren't special enough to break the rules
  • 2
    If the implementation is hard to explain, it's a bad id
  • 2
    If the implementation is easy to explain, it may be a g
  • 2
    Simple and easy to learn
  • 2
    Import this
  • 2
    No cruft
  • 2
    Easy to learn and use
  • 1
    Flexible and easy
  • 1
    Batteries included
  • 1
    Powerful language for AI
  • 1
    Should START with this but not STICK with This
  • 1
    Good
  • 1
    It is Very easy , simple and will you be love programmi
  • 1
    Better outcome
  • 1
    إسلام هشام
  • 1
    Because of Netflix
  • 1
    A-to-Z
  • 1
    Only one way to do it
  • 1
    Pip install everything
  • 0
    Powerful
  • 0
    Pro
CONS OF PYTHON
  • 51
    Still divided between python 2 and python 3
  • 29
    Performance impact
  • 26
    Poor syntax for anonymous functions
  • 21
    GIL
  • 19
    Package management is a mess
  • 14
    Too imperative-oriented
  • 12
    Dynamic typing
  • 12
    Hard to understand
  • 10
    Very slow
  • 8
    Not everything is expression
  • 7
    Indentations matter a lot
  • 7
    Explicit self parameter in methods
  • 6
    No anonymous functions
  • 6
    Poor DSL capabilities
  • 6
    Incredibly slow
  • 6
    Requires C functions for dynamic modules
  • 5
    The "lisp style" whitespaces
  • 5
    Fake object-oriented programming
  • 5
    Hard to obfuscate
  • 5
    Threading
  • 4
    Circular import
  • 4
    The benevolent-dictator-for-life quit
  • 4
    Official documentation is unclear.
  • 4
    Lack of Syntax Sugar leads to "the pyramid of doom"
  • 4
    Not suitable for autocomplete
  • 2
    Meta classes
  • 1
    Training wheels (forced indentation)

related Python posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 40 upvotes · 4.8M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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Nick Parsons
Director of Developer Marketing at Stream · | 35 upvotes · 1.6M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

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R Language logo

R Language

2.5K
1.6K
389
A language and environment for statistical computing and graphics
2.5K
1.6K
+ 1
389
PROS OF R LANGUAGE
  • 80
    Data analysis
  • 61
    Graphics and data visualization
  • 52
    Free
  • 43
    Great community
  • 37
    Flexible statistical analysis toolkit
  • 26
    Access to powerful, cutting-edge analytics
  • 25
    Easy packages setup
  • 18
    Interactive
  • 11
    R Studio IDE
  • 9
    Hacky
  • 6
    Shiny apps
  • 5
    Preferred Medium
  • 5
    Shiny interactive plots
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 1
    Graphical visualization
  • 1
    Machine Learning
CONS OF R LANGUAGE
  • 4
    Very messy syntax
  • 4
    Tables must fit in RAM
  • 2
    Arrays indices start with 1
  • 2
    No push command for vectors/lists
  • 2
    Messy syntax for string concatenation
  • 1
    Messy character encoding
  • 0
    Poor syntax for classes
  • 0
    Messy syntax for array/vector combination

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Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.3M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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Maged Maged Rafaat Kamal
Shared insights
on
PythonPythonR LanguageR Language

I am currently trying to learn R Language for machine learning, I already have a good knowledge of Python. What resources would you recommend to learn from as a beginner in R?

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MATLAB logo

MATLAB

718
592
31
A high-level language and interactive environment for numerical computation, visualization, and programming
718
592
+ 1
31
PROS OF MATLAB
  • 16
    Simulink
  • 5
    Functions, statements, plots, directory navigation easy
  • 3
    Model based software development
  • 3
    S-Functions
  • 2
    REPL
  • 1
    Simple variabel control
  • 1
    Solve invertible matrix
CONS OF MATLAB
  • 1
    Parameter-value pairs syntax to pass arguments clunky
  • 0
    Does not support named function arguments
  • 0
    Doesn't allow unpacking tuples/arguments lists with *

related MATLAB posts

Rust logo

Rust

3.3K
3.6K
1.2K
A safe, concurrent, practical language
3.3K
3.6K
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1.2K
PROS OF RUST
  • 137
    Guaranteed memory safety
  • 125
    Fast
  • 82
    Open source
  • 75
    Minimal runtime
  • 69
    Pattern matching
  • 61
    Type inference
  • 55
    Concurrent
  • 54
    Algebraic data types
  • 45
    Efficient C bindings
  • 43
    Practical
  • 37
    Best advances in languages in 20 years
  • 29
    Safe, fast, easy + friendly community
  • 29
    Fix for C/C++
  • 23
    Stablity
  • 22
    Closures
  • 21
    Zero-cost abstractions
  • 19
    Extensive compiler checks
  • 18
    Great community
  • 15
    No NULL type
  • 14
    Async/await
  • 14
    Completely cross platform: Windows, Linux, Android
  • 13
    No Garbage Collection
  • 12
    Great documentations
  • 12
    High-performance
  • 11
    Super fast
  • 11
    High performance
  • 10
    Fearless concurrency
  • 10
    Generics
  • 10
    Safety no runtime crashes
  • 9
    Helpful compiler
  • 9
    Compiler can generate Webassembly
  • 9
    Guaranteed thread data race safety
  • 8
    Easy Deployment
  • 8
    Macros
  • 8
    Prevents data races
  • 7
    RLS provides great IDE support
  • 7
    Painless dependency management
  • 6
    Real multithreading
  • 4
    Support on Other Languages
  • 4
    Good package management
CONS OF RUST
  • 25
    Hard to learn
  • 23
    Ownership learning curve
  • 10
    Unfriendly, verbose syntax
  • 4
    High size of builded executable
  • 4
    Variable shadowing
  • 4
    Many type operations make it difficult to follow
  • 3
    No jobs

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James Cunningham
Operations Engineer at Sentry · | 18 upvotes · 120.5K views
Shared insights
on
PythonPythonRustRust
at

Sentry's event processing pipeline, which is responsible for handling all of the ingested event data that makes it through to our offline task processing, is written primarily in Python.

For particularly intense code paths, like our source map processing pipeline, we have begun re-writing those bits in Rust. Rust’s lack of garbage collection makes it a particularly convenient language for embedding in Python. It allows us to easily build a Python extension where all memory is managed from the Python side (if the Python wrapper gets collected by the Python GC we clean up the Rust object as well).

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Jakub Olan
Node.js Software Engineer · | 17 upvotes · 307.2K views

In our company we have think a lot about languages that we're willing to use, there we have considering Java, Python and C++ . All of there languages are old and well developed at fact but that's not ideology of araclx. We've choose a edge technologies such as Node.js , Rust , Kotlin and Go as our programming languages which is some kind of fun. Node.js is one of biggest trends of 2019, same for Go. We want to grow in our company with growth of languages we have choose, and probably when we would choose Java that would be almost impossible because larger languages move on today's market slower, and cannot have big changes.

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Golang logo

Golang

15.2K
12.3K
3.2K
An open source programming language that makes it easy to build simple, reliable, and efficient software
15.2K
12.3K
+ 1
3.2K
PROS OF GOLANG
  • 530
    High-performance
  • 387
    Simple, minimal syntax
  • 354
    Fun to write
  • 295
    Easy concurrency support via goroutines
  • 267
    Fast compilation times
  • 189
    Goroutines
  • 177
    Statically linked binaries that are simple to deploy
  • 148
    Simple compile build/run procedures
  • 134
    Backed by google
  • 131
    Great community
  • 50
    Garbage collection built-in
  • 42
    Built-in Testing
  • 41
    Excellent tools - gofmt, godoc etc
  • 38
    Elegant and concise like Python, fast like C
  • 34
    Awesome to Develop
  • 25
    Used for Docker
  • 24
    Flexible interface system
  • 22
    Great concurrency pattern
  • 22
    Deploy as executable
  • 19
    Open-source Integration
  • 16
    Fun to write and so many feature out of the box
  • 15
    Easy to read
  • 14
    Its Simple and Heavy duty
  • 14
    Go is God
  • 13
    Powerful and simple
  • 13
    Easy to deploy
  • 11
    Concurrency
  • 11
    Best language for concurrency
  • 10
    Safe GOTOs
  • 10
    Rich standard library
  • 9
    Clean code, high performance
  • 9
    Easy setup
  • 8
    Simplicity, Concurrency, Performance
  • 8
    High performance
  • 8
    Hassle free deployment
  • 7
    Used by Giants of the industry
  • 7
    Single binary avoids library dependency issues
  • 6
    Cross compiling
  • 6
    Simple, powerful, and great performance
  • 5
    Excellent tooling
  • 5
    Very sophisticated syntax
  • 5
    Gofmt
  • 5
    WYSIWYG
  • 5
    Garbage Collection
  • 4
    Widely used
  • 4
    Kubernetes written on Go
  • 3
    Keep it simple and stupid
  • 1
    No generics
  • 1
    Operator goto
CONS OF GOLANG
  • 41
    You waste time in plumbing code catching errors
  • 25
    Verbose
  • 22
    Packages and their path dependencies are braindead
  • 15
    Google's documentations aren't beginer friendly
  • 15
    Dependency management when working on multiple projects
  • 10
    Automatic garbage collection overheads
  • 8
    Uncommon syntax
  • 6
    Type system is lacking (no generics, etc)
  • 2
    Collection framework is lacking (list, set, map)

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 40 upvotes · 4.8M views

How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

https://eng.uber.com/distributed-tracing/

(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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Nick Parsons
Director of Developer Marketing at Stream · | 35 upvotes · 1.6M views

Winds 2.0 is an open source Podcast/RSS reader developed by Stream with a core goal to enable a wide range of developers to contribute.

We chose JavaScript because nearly every developer knows or can, at the very least, read JavaScript. With ES6 and Node.js v10.x.x, it’s become a very capable language. Async/Await is powerful and easy to use (Async/Await vs Promises). Babel allows us to experiment with next-generation JavaScript (features that are not in the official JavaScript spec yet). Yarn allows us to consistently install packages quickly (and is filled with tons of new tricks)

We’re using JavaScript for everything – both front and backend. Most of our team is experienced with Go and Python, so Node was not an obvious choice for this app.

Sure... there will be haters who refuse to acknowledge that there is anything remotely positive about JavaScript (there are even rants on Hacker News about Node.js); however, without writing completely in JavaScript, we would not have seen the results we did.

#FrameworksFullStack #Languages

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NumPy logo

NumPy

1.4K
672
8
Fundamental package for scientific computing with Python
1.4K
672
+ 1
8
PROS OF NUMPY
  • 7
    Great for data analysis
  • 1
    Faster than list
CONS OF NUMPY
    Be the first to leave a con

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    Server side

    We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

    • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

    • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

    • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

    Client side

    • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

    • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

    • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

    Cache

    • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

    Database

    • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

    Infrastructure

    • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

    Other Tools

    • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

    • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

    See more
    JavaScript logo

    JavaScript

    252.9K
    198.8K
    7.8K
    Lightweight, interpreted, object-oriented language with first-class functions
    252.9K
    198.8K
    + 1
    7.8K
    PROS OF JAVASCRIPT
    • 1.6K
      Can be used on frontend/backend
    • 1.5K
      It's everywhere
    • 1.1K
      Lots of great frameworks
    • 886
      Fast
    • 735
      Light weight
    • 416
      Flexible
    • 385
      You can't get a device today that doesn't run js
    • 284
      Non-blocking i/o
    • 233
      Ubiquitousness
    • 188
      Expressive
    • 51
      Extended functionality to web pages
    • 44
      Relatively easy language
    • 42
      Executed on the client side
    • 26
      Relatively fast to the end user
    • 22
      Pure Javascript
    • 17
      Functional programming
    • 11
      Async
    • 8
      Setup is easy
    • 7
      Its everywhere
    • 7
      Because I love functions
    • 7
      JavaScript is the New PHP
    • 7
      Like it or not, JS is part of the web standard
    • 7
      Full-stack
    • 6
      Expansive community
    • 6
      Future Language of The Web
    • 6
      Can be used in backend, frontend and DB
    • 5
      Evolution of C
    • 5
      Everyone use it
    • 5
      Love-hate relationship
    • 5
      Easy to hire developers
    • 5
      Supports lambdas and closures
    • 5
      Agile, packages simple to use
    • 5
      Popularized Class-Less Architecture & Lambdas
    • 5
      For the good parts
    • 4
      Function expressions are useful for callbacks
    • 4
      Everywhere
    • 4
      Hard not to use
    • 4
      Promise relationship
    • 4
      Scope manipulation
    • 4
      It's fun
    • 4
      Client processing
    • 4
      Nice
    • 4
      Easy to make something
    • 4
      Can be used on frontend/backend/Mobile/create PRO Ui
    • 4
      Can be used both as frontend and backend as well
    • 4
      Photoshop has 3 JS runtimes built in
    • 4
      Most Popular Language in the World
    • 4
      1.6K Can be used on frontend/backend
    • 4
      Stockholm Syndrome
    • 4
      What to add
    • 4
      Clojurescript
    • 4
      No need to use PHP
    • 4
      Its fun and fast
    • 4
      Powerful
    • 4
      Versitile
    • 4
      Easy
    • 4
      It let's me use Babel & Typescript
    • 4
      Client side JS uses the visitors CPU to save Server Res
    • 3
      Only Programming language on browser
    • 3
      Because it is so simple and lightweight
    • 2
      JavaScript j.s
    • 2
      Acoperișul 0757604335
    • 0
      Easy to understand
    CONS OF JAVASCRIPT
    • 21
      A constant moving target, too much churn
    • 20
      Horribly inconsistent
    • 14
      Javascript is the New PHP
    • 8
      No ability to monitor memory utilitization
    • 6
      Shows Zero output in case of ANY error
    • 5
      Can be ugly
    • 4
      Thinks strange results are better than errors
    • 2
      No GitHub
    • 1
      Slow

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    Zach Holman

    Oof. I have truly hated JavaScript for a long time. Like, for over twenty years now. Like, since the Clinton administration. It's always been a nightmare to deal with all of the aspects of that silly language.

    But wowza, things have changed. Tooling is just way, way better. I'm primarily web-oriented, and using React and Apollo together the past few years really opened my eyes to building rich apps. And I deeply apologize for using the phrase rich apps; I don't think I've ever said such Enterprisey words before.

    But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

    Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 40 upvotes · 4.8M views

    How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

    Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

    Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

    https://eng.uber.com/distributed-tracing/

    (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

    Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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    PHP logo

    PHP

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    62.9K
    4.6K
    A popular general-purpose scripting language that is especially suited to web development
    119.8K
    62.9K
    + 1
    4.6K
    PROS OF PHP
    • 945
      Large community
    • 808
      Open source
    • 762
      Easy deployment
    • 481
      Great frameworks
    • 385
      The best glue on the web
    • 234
      Continual improvements
    • 182
      Good old web
    • 144
      Web foundation
    • 134
      Community packages
    • 124
      Tool support
    • 34
      Used by wordpress
    • 33
      Excellent documentation
    • 28
      Used by Facebook
    • 23
      Because of Symfony
    • 21
      Dynamic Language
    • 16
      Cheap hosting
    • 14
      Very powerful web language
    • 14
      Easy to learn
    • 14
      Fast development
    • 14
      Awesome Language and easy to implement
    • 12
      Composer
    • 10
      Because of Laravel
    • 10
      Flexibility, syntax, extensibility
    • 8
      Easiest deployment
    • 7
      Fastestest Time to Version 1.0 Deployments
    • 7
      Worst popularity quality ratio
    • 7
      Short development lead times
    • 7
      Readable Code
    • 6
      Most of the web uses it
    • 6
      Faster then ever
    • 6
      Fast
    • 5
      Simple, flexible yet Scalable
    • 5
      Open source and large community
    • 4
      I have no choice :(
    • 4
      Has the best ecommerce(Magento,Prestashop,Opencart,etc)
    • 4
      Is like one zip of air
    • 4
      Open source and great framework
    • 4
      Large community, easy setup, easy deployment, framework
    • 4
      Easy to use and learn
    • 4
      Cheap to own
    • 4
      Easy to learn, a big community, lot of frameworks
    • 3
      Great developer experience
    • 2
      Hard not to use
    • 2
      FFI
    • 2
      Interpreted at the run time
    • 2
      Great flexibility. From fast prototyping to large apps
    • 2
      Used by STOMT
    • 2
      Fault tolerance
    • 2
      Safe the planet
    • 2
      Walk away
    CONS OF PHP
    • 20
      So easy to learn, good practices are hard to find
    • 16
      Inconsistent API
    • 8
      Fragmented community
    • 5
      Not secure
    • 2
      No routing system
    • 1
      Hard to debug
    • 1
      Old

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    Our whole Node.js backend stack consists of the following tools:

    • Lerna as a tool for multi package and multi repository management
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    • NestJS as Node.js framework
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    The main reason we have chosen Node.js over PHP is related to the following artifacts:

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